T. 2007. Relational Learning by Observation, Ph.D.
Thesis, Electrical Engineering and Computer Science, University of Michigan.
Chair : John E. Laird
In this dissertation, we investigate learning by observation, a machine
learning approach to create cognitive agents automatically by observing the
task-performance behavior of human experts. We argue that the most important
challenge of learning by observation is that the internal reasoning of the
expert is not available to the learner. As a solution, we propose a
framework that uses multiple complex knowledge sources to model the expert
We describe a relational learning by observation framework that uses expert
behavior traces and expert goal annotations as the primary input, interprets
them in the context of background knowledge, inductively finds patterns in
similar expert decisions, and creates an agent program. The background
knowledge used to interpret the expert behavior does not only include task
and domain knowledge, but also domain independent learning by observation
knowledge that models the fixed mental mechanisms of the expert.
We explore two learning approaches. In learning from behavior performances approach, the main source of information used in learning is behavior traces
of expert recorded during actual task performance. In the learning from
diagrammatic behavior specifications approach, the expert specifies behavior
using a graphical representation, abstractly depicting the critical
situations for the desired behavior. This provides the expert with additional modes of interaction with the learning system; simplifying the
learning task at the expense of more expert effort. Both of these approaches
are uniformly represented in relational learning by observation framework.
Our framework maps "learning an agent program" problem on to
learning problems that can be represented in a "supervised concept
setting. The acquired procedural knowledge is partitioned into a hierarchy
of goals and it is represented with first order rules. Using an inductive
logic programming (ILP) learning component allows our system to combine
complex knowledge from multiple sources. These sources include the behavior
traces, which are temporally changing relational situations, the expert goal
annotations, which are hierarchically organized and provide structured
information, and background knowledge, which is represented as relational
facts and first order rules.
Our learning by observation framework needs to store large amounts of
behavior data and access it efficiently during learning. We propose an
episodic database as a solution, which is an extension of Prolog that
improves Prolog by providing efficient and power mechanisms to store and
query relational temporal information.
We evaluated our framework using both artificially created examples and
behavior observation traces generated by AI agents. We developed a general
methodology to test relational learning by observation. Our methodology is
based on first using a hand-coded agent program as the expert, and then
comparing the decision making knowledge of the expert and learned agent
programs on observed situations.